15 citations · 40 across the 4 of their papers we have counts for
5 papers
Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation
Lars Schmarje, Vasco Grossmann, Claudius Zelenka +8
High-quality data is necessary for modern machine learning. However, the acquisition of such data is difficult due to noisy and ambiguous annotations of humans. The aggregation of…
Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy
Lars Schmarje, Johannes Brünger, Monty Santarossa +3
Deep learning has been successfully applied to many classification problems including underwater challenges. However, a long-standing issue with deep learning is the need for large…
A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering
Lars Schmarje, Monty Santarossa, Simon-Martin Schröder +5
Consistently high data quality is essential for the development of novel loss functions and architectures in the field of deep learning. The existence of such data and labels is us…
Beyond Cats and Dogs: Semi-supervised Classification of fuzzy labels with overclustering
Lars Schmarje, Johannes Brünger, Monty Santarossa +3
A long-standing issue with deep learning is the need for large and consistently labeled datasets. Although the current research in semi-supervised learning can decrease the require…
MorphoCluster: Efficient Annotation of Plankton images by Clustering
Simon-Martin Schröder, Rainer Kiko, Reinhard Koch
In this work, we present MorphoCluster, a software tool for data-driven, fast and accurate annotation of large image data sets. While already having surpassed the annotation rate o…